Focused ion beam scanning electron microscopy (FIB-SEM), also referred to as ion abrasion scanning electron microscopy (IA-SEM), is a technology that we have been developing in the lab to image cells and tissues in 3D at high resolution. Imaging cells and tissues by FIB-SEM at high resolution offers many exciting possibilities for biological research; however, at high resolution, this technology produces enormous amounts of data, and is extremely slow. Moreover, one of the most promising aspects of this technology is the ability to quantitatively analyze ultrastructural morphology. Thus in addition to using FIB-SEM to study 3D architecture in cells and tissues, we have also been developing imaging methods and techniques that align the technology with the goal of automated, quantitative analysis of 3D structure at electron microscopy resolutions. As we make progress on a variety of projects related to cancer and aging, we have also been developing methods to process FIB-SEM data more effectively. In a collaboration with Dr. Amitabh Varshney's team at the University of Maryland, we published a paper in January 2-18 in IEEE Transactions in Visualization and Computer Graphics that reports the use of deep learning methods for 3D image segmentation. Designing volume visualizations showing various structures of interest is critical to the exploratory analysis of volumetric data. The last few years have witnessed dramatic advances in the use of convolutional neural networks for identification of objects in large image collections. Whereas such machine learning methods have shown superior performance in a number of applications, their direct use in volume visualization has not yet been explored. In the IEEE paper, we presented a deep-learning-assisted volume visualization to depict complex structures, which are otherwise challenging for conventional approaches. A significant challenge in designing volume visualizations based on the high-dimensional deep features lies in efficiently handling the immense amount of information that deep-learning methods provide. In this paper, we present a new technique that uses spectral methods to facilitate user interactions with high-dimensional features. We also present a new deep-learning-assisted technique for hierarchically exploring a volumetric dataset. We validated our approach with two volumes generated using electron microscopy and one with magnetic resonance imaging.